Introduction

In recent years, the relationship between economic indicators and health outcomes has become a focal point of research, especially within the context of OECD countries. This study aims to explore two critical economic aspects: Gross Net Income (GNI) per Capita and Health Expenditure as a percentage of GDP and the impact these measures have on health outcomes. Our research is guided by two primary questions. Firstly, we investigate the relationship between GNI Per Capita and the infant mortality rate among children under 5 years. Secondly, we explore the correlation between GNI Per Capita and life expectancy at birth. The significance of this study lies in its potential to uncover the intricacies of how economic prosperity, as measured by GNI per capita, influences essential health metrics like infant mortality and life expectancy.

Moreover, the study investigates the correlation between health expenditure and key health indicators in OECD countries. We analyze how health expenditure as a percentage of GDP relates to life expectancy at birth and infant mortality rates under 5 years old. This section includes a diverse array of OECD countries, encompassing varied healthcare systems and recent health reforms. Our goal is to discern the impact of healthcare investments on life expectancy and infant mortality, and explore the effectiveness of different healthcare structures. By examining these relationships, we aim to shed light on the interplay between financial investment in healthcare and its tangible outcomes in different region, offering insights into the nuanced roles of healthcare spending and system efficiency on health outcomes.

For our analysis, we utilised the World Development Indicators dataset provided by the World Bank. We focused on data spanning from 2000 to 2020 for OECD countries, with the exception of Greece, where data was excluded due to missing values. The variables considered for our analysis include:

Table 1: Variable Descriptions
Variable_Name Description
Year Year of Observation
Country_Name Name of Country
LifeExp_Total Life expectancy at birth: the expected lifespan of a newborn based on prevailing mortality patterns at the time of birth.
GNIPerCapita GNI per capita is the gross national income, converted to U.S. dollars using the World Bank Atlas method, divided by the midyear population. 
Mortality_U5_Total Under-five mortality rate is the probability per 1,000 that a newborn baby will die before reaching age five, if subject to age-specific mortality rates of the specified year.
HealthExp_percent_GDP Level of current health expenditure expressed as a percentage of GDP.

Analysis by Mitch

Gross Net Income (GNI) and other factors have been found to be strong determinants of a populations health (Jalal et al., 2016). In this analysis we aim to explore the relationship between Gross Net Income (GNI) per Capita and key health indicators, specifically focusing on infant mortality and life expectancy The two research questions are as follows

  1. Is there a relationship between GNI Per Capita and the infant mortality rate among children aged less than 5 years in OECD Countries?

  2. Is there a relationship between GNI Per Capita and life expectancy at birth of countries in the OECD?

Visualisations

The visualisations comprises two scatterplots that highlight the relationship between GNI Per Capita and the health indicators— mortality rates among children under 5 (infant mortality rates) and life expectancy. The visualisations encompasses all OECD countries from 2000 to 2022.

The Figure 1 unveils a downward trend in infant mortality rates, suggesting that as GNI Per Capita increases, there is a corresponding decline in infant mortality rates.

p <- df %>% 
  ggplot(aes(x = GNIPerCapita, y = Mortality_U5_Total,colour=Country_Name)) + 
  geom_point(size=0.8) +
  geom_smooth(method = "loess", se = FALSE, color = "blue") +
  theme_minimal() +
  labs(title="Scatter Plot of Mortality Rate Under 5 years and GNI Per Capita",
       subtitle="OECD Countries from 2000 to 2022",
       y="Mortality Rate Under 5 years",
       x="GNI Per Capita US$")

p %>% ggplotly()

Figure 1: Scatter Plot of Mortality Rate Under 5 years and GNI Per Capita

Moving on to Figure 2, the plot illustrates an upward trend in life expectancy, indicating that as GNI Per Capita rises, life expectancy at birth also tends to increase.

p2 <- df %>%
  ggplot(aes(x = GNIPerCapita, y = LifeExp_Total, colour=Country_Name)) + 
  geom_point(size=0.8) +
  geom_smooth(method = "loess", se = FALSE, color = "blue") +
  theme_minimal() +
  labs(title="Scatter Plot of Life Expectancy and GNI Per Capita",
       subtitle="OECD Countries from 2000 to 2022",
       y="Life Expectancy at birth",
       x="GNI Per Capita US$") 
p2 %>% ggplotly()

Figure 2: Scatter Plot of Life Expectancy and GNI Per Capita

Tables

While the earlier scatterplots illustrated general trends in these relationships between GNI per Capita, Life Expectancy and Mortality, we will now quantify them to provide a more precise understanding of the associations. To delve deeper into these relationships, we focused on 20 Countries with the Highest and Lowest Average GNI Per Capita across the 20-year period.

df %>% group_by(Country_Name) %>%
  summarise("Avg GNI Per Capita" = round(mean(GNIPerCapita),2),
            "Avg Life Expectancy" = round(mean(LifeExp_Total),2),
            "Avg Mortality U5" = round(mean(Mortality_U5_Total),2),
            Mortality_Correlation = round(cor(GNIPerCapita, Mortality_U5_Total, use = "complete.obs"),3),
            LifeExp_Correlation = round(cor(GNIPerCapita, LifeExp_Total, use = "complete.obs"),3)) %>%
  arrange(`Avg GNI Per Capita`) %>%
  mutate("Rank Life Expectancy" = rank(-`Avg Life Expectancy`),
         "Rank Mortality U5" = rank(`Avg Mortality U5`)) %>%
  arrange(-`Avg GNI Per Capita`) %>% head(10) %>% kable(caption="Countries with 10 Highest Average GNI Per Capita") %>% kable_styling(full_width = FALSE, position = "left")
Table 2: Countries with 10 Highest Average GNI Per Capita
Country_Name Avg GNI Per Capita Avg Life Expectancy Avg Mortality U5 Mortality_Correlation LifeExp_Correlation Rank Life Expectancy Rank Mortality U5
Norway 75527.14 81.03 3.40 -0.800 0.750 10 6
Luxembourg 71690.48 80.52 3.17 -0.897 0.775 13 3
Switzerland 70606.67 82.13 4.66 -0.927 0.928 2 19
Denmark 53603.81 79.18 4.43 -0.962 0.834 23 14
United States 50584.76 78.01 7.36 -0.971 0.678 27 31
Sweden 49786.67 81.36 3.22 -0.901 0.862 7 4
Iceland 47820.48 81.85 2.94 -0.574 0.661 3 1
Netherlands 44995.71 80.39 4.73 -0.900 0.879 14 20
Ireland 44944.29 80.16 4.58 -0.904 0.896 16 16
Australia 43544.76 81.52 4.83 -0.932 0.905 6 22

Referring to the Table 2 we can see that as GNI per capita increases, there is a tendency for mortality rates to decrease. The negative correlations for mortality rates for these countries range from -0.574 to -0.971, indicating a strong negative association between GNI per capita and mortality rates. The relationship between GNI per Capita and Life Expectancy is positive across the selected countries. As GNI per capita increases, there is a general tendency for life expectancy to rise with the correlations ranging from 0.661 to 0.928, indicating a strong positive association between GNI per capita and life expectancy. The higher correlations suggest the significant impact of economic prosperity on increasing life expectancy.

Specific Country Insights:

df %>% group_by(Country_Name) %>%
  summarise("Avg GNI Per Capita" = round(mean(GNIPerCapita),2),
            "Avg Life Expectancy" = round(mean(LifeExp_Total),2),
            "Avg Mortality U5" = round(mean(Mortality_U5_Total),2),
            Mortality_Correlation = round(cor(GNIPerCapita, Mortality_U5_Total, use = "complete.obs"),3),
            LifeExp_Correlation = round(cor(GNIPerCapita, LifeExp_Total, use = "complete.obs"),3)) %>%
  arrange(`Avg GNI Per Capita`) %>%
  mutate("Rank Life Expectancy" = rank(-`Avg Life Expectancy`),
         "Rank Mortality U5" = rank(`Avg Mortality U5`)) %>%
  arrange(`Avg GNI Per Capita`) %>% head(10) %>% kable(caption="Countries with 10 Lowest Average GNI Per Capita") %>% kable_styling(full_width = FALSE, position = "left")
Table 3: Countries with 10 Lowest Average GNI Per Capita
Country_Name Avg GNI Per Capita Avg Life Expectancy Avg Mortality U5 Mortality_Correlation LifeExp_Correlation Rank Life Expectancy Rank Mortality U5
Colombia 5101.90 74.51 18.66 -0.867 0.921 33 35
Costa Rica 7634.76 78.75 10.33 -0.975 0.909 25 34
Turkiye 8786.67 74.94 20.17 -0.888 0.856 31 37
Mexico 8903.81 74.08 19.80 -0.813 0.249 35 36
Chile 10355.71 78.71 8.63 -0.862 0.924 26 33
Poland 10706.19 76.09 6.30 -0.961 0.895 29 27
Latvia 11587.62 72.96 8.07 -0.964 0.912 37 32
Hungary 11609.52 74.20 6.40 -0.954 0.874 34 28
Lithuania 11727.14 73.24 6.75 -0.980 0.832 36 29
Estonia 14206.19 74.88 5.34 -0.980 0.969 32 24

In the next Table 3, it shows that as GNI per capita decreases, there is a tendency for infant mortality rates to increase. The negative correlations for these countries range from -0.813 to -0.980, indicating a strong negative association between lower GNI per capita and higher mortality rates. The relationship between GNI per Capita and Life Expectancy is positive, suggesting that as GNI per capita increases, there is a tendency for life expectancy to increase as well. Notably Mexicos correlation of 0.249 appears to be an outlier of the group with others showing strong correlations of greater than 0.8. Additionally, the analysis reveals that the 5 countries with the lowest Average GNI per Capita exhibit the lowest (unordered) ranks for Mortality and also demonstrate lower ranks for Life Expectancy. These findings suggest an association between lower GNI per Capita and poorer health outcomes in relation to Life Expectancy and Infant Mortality.

Specific Country Insights:

Analysis by Hongmei

In this section, our research concentrates on examining the correlation between health expenditure as a percentage of GDP and overall life expectancy at birth to understand if higher healthcare investment translates into longer life spans. On the other hand, a specific focus also will be placed on exploring a comparative analysis of a select group of representative OECD countries, focusing on how their health expenditure influences child mortality rates under the age of 5, this comparative study aims to uncover regional differences and insights into how distinct health system structures and spending levels impact crucial health outcomes, specifically in child health.

  1. The relationship between health expenditure with life expectation in OECD
# Plotting the relationship between health expenditure and life expectation for each year
p <- ggplot(df, aes(x = HealthExp_percent_GDP, y = LifeExp_Total, color = Country_Name)) +
  geom_point(size=1) +
  geom_smooth(method = "lm",  formula = y ~ x, se = FALSE, color = "blue") +
  labs(title = "Health Expenditure vs Life Expectation in OECD Countries",
       x = "Health Expenditure as % of GDP", 
       y = "Life Expectation at Birth") +
  theme_minimal()
# Convert the ggplo to an interactive plot
interactive_plot <- ggplotly(p)
interactive_plot

Figure 3: Health Expenditure vs Life Expectation at Birth in OECD Countries

As show in Figure 3, it illustrates a discernible positive relationship between health expenditure as a percentage of GDP and life expectancy at birth in OECD countries. This trend implies a potential association between greater health care investment and improved health outcomes, as evidenced by increased life expectancy.

  1. The relationship between health expenditure with mortality rate under 5 in representative countries
representative_countries <- c("USA", "CAN", "GBR", "DEU", "SWE",
                              "NZL", "MEX", "JPN", "KOR", "AUS")
# Filter the dataset
filtered_data <- df %>%
                 filter(Country_Code %in% representative_countries)
# Plot the relationship between health expenditure and mortality rate in representative countries
p1 <- ggplot(filtered_data, aes(x = HealthExp_percent_GDP, y = Mortality_U5_Total, text = Country_Name)) +
     geom_line(aes(color = Country_Name)) +
     labs(title = "Health Expenditure vs Mortality Rate in representative countries of OECD",
          x = "Health Expenditure",
          y = "Mortality Rate Under 5") +
     theme_minimal()
interactive_plot1 <- ggplotly(p1, tooltip = c("text", "x", "y"))
interactive_plot1

Figure 4: Health Expenditure vs Mortality Rate in representative countries of OECD

In the Figure 4, ten representative OECD countries were carefully selected to encompass a variety of healthcare systems, including universal healthcare, insurance-based, and mixed systems. The selection also includes countries that have undergone significant health reforms, such as Mexico and South Korea, and those with notable demographic characteristics, like Japan’s large population and aging society. This figure illustrates the relationship between health expenditure and infant mortality rates in these countries and most of countries health expenditure is between 7.5% and 12.5%. A general trend is observed where higher health expenditure correlates with lower infant mortality rates, indicating a significant negative correlation and highlighting the impact of financial investment on health outcomes in most of countries. Notably, the USA stands out with its higher level of health expenditure compared to other countries. However, Mexico deviates from this general trend, exhibiting a fluctuating relationship and a higher infant mortality rate than might be expected given its health expenditure level. This anomaly suggests that factors other than expenditure, such as efficiency of healthcare delivery or socio-economic conditions, may also play a critical role in determining health outcomes.

  1. Summary statistics of health expenditure and health outcome indicators in the selected countries
# Calculate summary statistics
summary_stats <- filtered_data %>%
  group_by(Country_Name) %>%
  summarise(across(c(HealthExp_percent_GDP, Mortality_U5_Total, LifeExp_Total),
                   list(mean = ~ mean(., na.rm = TRUE), var = ~ var(., na.rm = TRUE))))
# Make column name shorter and meaningful
colnames(summary_stats) <- c("Country", "Mean_HealthExp", "Var_HealthExp", 
                             "Mean_MortU5", "Var_MortU5", "Mean_LifeExp", "Var_LifeExp")
# Display the table
kable(summary_stats, caption = "Summary Statistics of Health Expenditure and Health Indicators in Selected Countries", format = "html") %>%
  kable_styling(full_width = TRUE)
Table 4: Summary Statistics of Health Expenditure and Health Indicators in Selected Countries
Country Mean_HealthExp Var_HealthExp Mean_MortU5 Var_MortU5 Mean_LifeExp Var_LifeExp
Australia 8.835345 1.0433705 4.828571 0.8391429 81.52056 1.2516815
Canada 10.077522 1.2059462 5.719048 0.1476190 80.95753 0.9415795
Germany 10.811329 0.5179457 4.342857 0.2835714 79.86249 1.2120146
Japan 9.186331 2.4167287 3.280952 0.3966190 82.89201 1.0666114
Korea, Rep.  5.841171 1.7191164 4.614286 2.0182857 80.00546 5.5113420
Mexico 5.585032 0.1675555 19.800000 19.3180000 74.07862 0.8985976
New Zealand 8.863087 0.6149196 6.095238 0.5914762 80.64070 1.3572151
Sweden 9.427667 2.0443653 3.219048 0.2436190 81.36376 1.0511929
United Kingdom 9.208468 1.2574710 5.266667 0.6413333 79.97224 1.5290854
United States 15.575834 2.0455827 7.361905 0.4604762 78.00999 0.5841826

Table 4 provides a summary of the mean and variance in health expenditure alongside key health indicators, presenting a thorough quantitative analysis. The USA stands out with the highest health expenditure, yet it also shows a relatively higher variance in this spending, as well as in mortality rates. In contrast, Canada displays high average health expenditure, but with much lower variance, demonstrating consistency in its healthcare investments. On the other hand, countries like Mexico and Korea present notable variances in their health expenditure, suggesting complex underlying dynamics that merit deeper investigation. Intriguingly, Japan achieves a higher life expectancy and lower mortality rate with moderate health expenditure, underscoring the potential efficiency of its insurance-based health system.

  1. Correlation between health expenditure with health outcome indicators
# Correlation between Health Expenditure, Mortality under 5 and Life Expectancy at birth
correlation_stats <- filtered_data %>%
    group_by(Country_Name) %>%
    summarise(
      Cor_Heal_Mort = cor(HealthExp_percent_GDP, Mortality_U5_Total, use = "complete.obs"),
      Cor_Heal_Life = cor(HealthExp_percent_GDP, LifeExp_Total, use = "complete.obs")
      )
kable(correlation_stats, caption = "Correlation between Health Expenditure, Mortality under 5 and Life Expectancy", format = "html") %>%
  kable_styling(full_width = TRUE)
Table 5: Correlation between Health Expenditure, Mortality under 5 and Life Expectancy
Country_Name Cor_Heal_Mort Cor_Heal_Life
Australia -0.9272202 0.8888673
Canada -0.9059984 0.8609486
Germany -0.8533175 0.8124925
Japan -0.9345406 0.9074473
Korea, Rep.  -0.9221641 0.9741415
Mexico -0.4719754 -0.1723541
New Zealand -0.7808487 0.8992428
Sweden -0.8681373 0.9005585
United Kingdom -0.8626432 0.8163209
United States -0.9287438 0.6117293


Table 5 reveals a general trend where health expenditure in most countries has a strong negative correlation with the mortality rate under 5 years and a positive correlation with life expectancy. However, there are notable exceptions. The United States, for example, despite its high health expenditure, shows a relatively lower positive correlation (0.6117293) with life expectancy. This suggests that increased spending does not correspond with a proportionate increase in life expectancy, possibly reflecting inefficiencies or other influencing factors. Mexico presents a distinct case, displaying a much weaker negative correlation with life expectancy (-0.1723541) and a less pronounced relationship between health expenditure and the reduction of mortality under 5 years. These anomalies in the Mexican context point towards potential systemic issues in healthcare delivery or socio-economic factors impacting health outcomes.

Conclusion

Our comprehensive analysis of the relationship between economic indicators and health outcomes within OECD countries from 2000 to 2022 has yielded insightful results. Our first analysis focusing on GNI per Capita resulted in a downward trend in infant mortality rates as GNI per Capita rises, underscoring the impact of economic prosperity on child health. Simultaneously, there was an upward trend in life expectancy with increasing GNI per Capita highlighting the positive association between economic well-being and overall life expectancy. The examination of the 10 countries with the lowest Average GNI per Capita further supported these trends, indicating that lower economic prosperity is linked to weaker life expectancy and higher infant mortality rates.

Furthermore, the examination of health expenditure as a percentage of GDP reveals a generally negative correlation with infant mortality rates and a positive correlation with life expectancy. This finding suggests that higher healthcare investment is beneficial, but the impact is not uniform across all OECD countries. The cases of the USA and Mexico are particularly illuminating. Despite high health expenditure, the USA shows a less pronounced increase in life expectancy at birth, while Mexico, with its unique challenges, demonstrates that high spending does not always translate into better health outcomes. These variations point to the necessity of considering factors beyond financial health investment, such as efficiency of healthcare delivery and socio-economic conditions.

In conclusion, this study provides critical evidence of the links between economic health indicators and health outcomes. While increased economic wealth and healthcare spending generally correlate with better health metrics, the relationship is nuanced and influenced by a multitude of factors. These findings emphasize the need for tailored health policies and investments by considering the specific contexts and challenges of each country to optimize health outcomes.

References

Bank, W. (2023). World development indicators. https://databank.worldbank.org/source/world-development-indicators
Jalal, S., Khan, N. U., & Younis, M. Z. (2016). Effect of GNI on infant mortality rate in low income, lower middle income, upper middle income and high income countries. J Health Hum Serv Adm, 39(2), 159–185.
Owusu, P. A., & Sarkodie, S. A. (2021). Relationship between mortality and health care expenditure: Sustainable assessment of healthcare system. https://doi.org/10.1371/journal.pone.0247413